




'''simple docstring'''







import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
    import jax

    from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
    from PIL import Image

    from transformers import BeitImageProcessor

class 	_snake_case(			unittest.TestCase      ):






 def __init__(self       :  Any    ,		a       :  Tuple    ,		a       :  str=1_00    ,		a       :  List[str]=13    ,		a       :  Union[str, Any]=30    ,		a       :  Union[str, Any]=2    ,		a       :  Optional[Any]=3    ,		a       :  Union[str, Any]=True    ,		a       :  Tuple=True    ,		a       :  Any=32    ,		a       :  Union[str, Any]=5    ,		a       :  List[str]=4    ,		a       :  str=37    ,		a       :  Union[str, Any]="gelu"    ,		a       :  List[Any]=0.1    ,		a       :  Optional[Any]=0.1    ,		a       :  Any=10    ,		a       :  Any=0.02    ,		a       :  Tuple=3    ,		)       ->   Dict:






    """simple docstring"""


    A__       =     parent
    A__       =     vocab_size
    A__       =     batch_size
    A__       =     image_size
    A__       =     patch_size
    A__       =     num_channels
    A__       =     is_training
    A__       =     use_labels
    A__       =     hidden_size
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     hidden_act
    A__       =     hidden_dropout_prob
    A__       =     attention_probs_dropout_prob
    A__       =     type_sequence_label_size
    A__       =     initializer_range

    # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    A__       =     (image_size // patch_size) ** 2
    A__       =     num_patches + 1






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Dict:






    """simple docstring"""


    A__       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

    A__       =     None
    if self.use_labels:
       A__       =     ids_tensor([self.batch_size]    ,		self.type_sequence_label_size    )

    A__       =     BeitConfig(
        vocab_size=self.vocab_size    ,		image_size=self.image_size    ,		patch_size=self.patch_size    ,		num_channels=self.num_channels    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		is_decoder=a    ,		initializer_range=self.initializer_range    ,		)

    return config, pixel_values, labels






 def 						_UpperCamelCase (self       :  str    ,		a       :  str    ,		a       :  Optional[int]    ,		a       :  str    )       ->   str:






    """simple docstring"""


    A__       =     FlaxBeitModel(config=a    )
    A__       =     model(a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  Any    ,		a       :  List[Any]    ,		a       :  Tuple    ,		a       :  str    )       ->   str:






    """simple docstring"""


    A__       =     FlaxBeitForMaskedImageModeling(config=a    )
    A__       =     model(a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length - 1, self.vocab_size)    )






 def 						_UpperCamelCase (self       :  Optional[Any]    ,		a       :  Optional[int]    ,		a       :  Any    ,		a       :  str    )       ->   Any:






    """simple docstring"""


    A__       =     self.type_sequence_label_size
    A__       =     FlaxBeitForImageClassification(config=a    )
    A__       =     model(a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)    )

    # test greyscale images
    A__       =     1
    A__       =     FlaxBeitForImageClassification(a    )

    A__       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
    A__       =     model(a    )






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    (
        (
        A__
    )  ,			(
        A__
    )  ,			(
        A__
    )  ,			
    )       =     config_and_inputs
    A__       =     {'pixel_values': pixel_values}
    return config, inputs_dict




@require_flax
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Any						=					(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )






 def 						_UpperCamelCase (self       :  List[str]    )       ->   None:






    """simple docstring"""


    A__       =     FlaxBeitModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		has_text_modality=a    ,		hidden_size=37    )






 def 						_UpperCamelCase (self       :  str    )       ->   Any:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  str    )       ->   Dict:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A__       =     model_class(a    )
       A__       =     inspect.signature(model.__call__    )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       A__       =     [*signature.parameters.keys()]

       A__       =     ['pixel_values']
       self.assertListEqual(arg_names[:1]    ,		a    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__    ):
          A__       =     self._prepare_for_class(a    ,		a    )
          A__       =     model_class(a    )

          @jax.jit
          def model_jitted(a       :  Dict    ,		**a       :  Dict    ):
             return model(pixel_values=a    ,		**a    )

          with self.subTest('JIT Enabled'    ):
             A__       =     model_jitted(**a    ).to_tuple()

          with self.subTest('JIT Disabled'    ):
             with jax.disable_jit():
                A__       =     model_jitted(**a    ).to_tuple()

          self.assertEqual(len(a    )    ,		len(a    )    )
          for jitted_output, output in zip(a    ,		a    ):
             self.assertEqual(jitted_output.shape    ,		output.shape    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*a    )






 @slow
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Any:






    """simple docstring"""


    for model_class_name in self.all_model_classes:
       A__       =     model_class_name.from_pretrained('microsoft/beit-base-patch16-224'    )
       A__       =     model(np.ones((1, 3, 2_24, 2_24)    )    )
       self.assertIsNotNone(a    )







def 						_A      ( ):






   '''simple docstring'''







   A__       =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image







@require_vision
@require_flax
class 	_snake_case(			unittest.TestCase      ):






 @cached_property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'    ) if is_vision_available() else None






 @slow
 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'    )

    A__       =     self.default_image_processor
    A__       =     prepare_img()
    A__       =     image_processor(images=a    ,		return_tensors='np'    ).pixel_values

    # prepare bool_masked_pos
    A__       =     np.ones((1, 1_96)    ,		dtype=a    )

    # forward pass
    A__       =     model(pixel_values=a    ,		bool_masked_pos=a    )
    A__       =     outputs.logits

    # verify the logits
    A__       =     (1, 1_96, 81_92)
    self.assertEqual(logits.shape    ,		a    )

    A__       =     np.array(
        [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]    )

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]    ,		a    ,		atol=1e-2    )    )






 @slow
 def 						_UpperCamelCase (self       :  Any    )       ->   int:






    """simple docstring"""


    A__       =     FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'    )

    A__       =     self.default_image_processor
    A__       =     prepare_img()
    A__       =     image_processor(images=a    ,		return_tensors='np'    )

    # forward pass
    A__       =     model(**a    )
    A__       =     outputs.logits

    # verify the logits
    A__       =     (1, 10_00)
    self.assertEqual(logits.shape    ,		a    )

    A__       =     np.array([-1.2385, -1.0987, -1.0108]    )

    self.assertTrue(np.allclose(logits[0, :3]    ,		a    ,		atol=1e-4    )    )

    A__       =     2_81
    self.assertEqual(logits.argmax(-1    ).item()    ,		a    )






 @slow
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'    )

    A__       =     self.default_image_processor
    A__       =     prepare_img()
    A__       =     image_processor(images=a    ,		return_tensors='np'    )

    # forward pass
    A__       =     model(**a    )
    A__       =     outputs.logits

    # verify the logits
    A__       =     (1, 2_18_41)
    self.assertEqual(logits.shape    ,		a    )

    A__       =     np.array([1.6881, -0.2787, 0.5901]    )

    self.assertTrue(np.allclose(logits[0, :3]    ,		a    ,		atol=1e-4    )    )

    A__       =     23_96
    self.assertEqual(logits.argmax(-1    ).item()    ,		a    )








'''simple docstring'''







import socket







def 						_A      ( ):






   '''simple docstring'''







   A__       =     socket.socket(socket.AF_INET      ,socket.SOCK_STREAM						)
   A__       =     socket.gethostname()
   A__       =     12312

   sock.connect((host, port)						)
   sock.send(B'Hello server!'						)

   with open('Received_file'      ,'wb'						) as out_file:
      print('File opened'						)
      print('Receiving data...'						)
      while True:
         A__       =     sock.recv(1024						)
         if not data:
            break
         out_file.write(UpperCAmelCase						)

   print('Successfully received the file'						)
   sock.close()
   print('Connection closed'						)


if __name__ == "__main__":
    main()


